The present invention relates generally to antiskid braking and traction control for a vehicle and, more particularly, to a system and method for providing antiskid braking and traction control in an electric or hybrid vehicle which improves control performance by the application of fuzzy logic.
Antiskid braking and traction control systems are well known and commonly employed in conventional internal combustion engine vehicles. However, the development of electric and hybrid vehicles has introduced concerns and opportunities unique to electric vehicle control system design. A primary area of concern in electric vehicle design, because of limited battery storage, is the amount of energy used by the various vehicle subsystems. Therefore, designing vehicle control systems that minimize energy use and conserve the available energy stored in the battery is critical.
A further opportunity unique to the electric or hybrid vehicle is the possibility of regenerating the kinetic energy dissipated during braking, or any other period in which the accelerator pedal is not depressed and the vehicle is in motion, e.g. coasting. Such regeneration can be accomplished by controlling the operation of the electric traction motor so that it behaves like a generator. The kinetic energy received during this process can be used to recharge the traction battery and stored for future use. Applying supplemental hydraulic braking only when the braking torque supplied by the electric traction motor cannot meet the driver's brake demand significantly increases the amount of energy recovered. The amount of kinetic energy that is wastefully dissipated while driving or launching an electric or hybrid vehicle is decreased if energy losses due to wheel slippage can be kept minimal.
An antiskid brake control system for a conventional vehicle based on fuzzy inference is disclosed in U.S. Pat. No. 4,842,342 issued to Takahashi et al. Various vehicle parameters are sensed and inputted to a control section for determining a manipulated quantity by a fuzzy inference. The brake fluid pressure is then modulated in accordance with the manipulated quantity. However, the Takahashi et al system was designed solely for operation in conventional vehicles having internal combustion engines and, as such, does not provide for regenerative braking control. In addition, Takahashi et al does not disclose a system for providing vehicle traction control.
Accordingly, there is a need for a system and method for providing antiskid braking and traction control in an electric or hybrid vehicle which provides maximum regenerated kinetic energy during braking and minimizes the loss of kinetic energy due to wheel slip using fuzzy logic to evaluate vehicle performance.